{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ae2c94a-ee91-49c3-a78d-6b97711b2fe4",
   "metadata": {},
   "source": [
    "### 案例分析——AdaBoost信用卡精准营销模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d45401d-cd7c-46fc-a473-7b1537b1dc6c",
   "metadata": {},
   "source": [
    "#### 首先读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e826b896-dd87-4423-9074-9ee321fe4e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>月收入（元）</th>\n",
       "      <th>月消费（元）</th>\n",
       "      <th>性别</th>\n",
       "      <th>月消费/月收入</th>\n",
       "      <th>响应</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>7275</td>\n",
       "      <td>6062</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833265</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>17739</td>\n",
       "      <td>13648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.769378</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>25736</td>\n",
       "      <td>14311</td>\n",
       "      <td>0</td>\n",
       "      <td>0.556069</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>14162</td>\n",
       "      <td>7596</td>\n",
       "      <td>0</td>\n",
       "      <td>0.536365</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>15563</td>\n",
       "      <td>12849</td>\n",
       "      <td>0</td>\n",
       "      <td>0.825612</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   年龄  月收入（元）  月消费（元）  性别   月消费/月收入  响应\n",
       "0  30    7275    6062   0  0.833265   1\n",
       "1  25   17739   13648   0  0.769378   1\n",
       "2  29   25736   14311   0  0.556069   1\n",
       "3  23   14162    7596   0  0.536365   1\n",
       "4  27   15563   12849   0  0.825612   1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('信用卡精准营销模型.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d15bb5f4-6bc9-4563-8489-33692d28c8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns='响应') \n",
    "y = df['响应']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0b8107a-a22e-4cdf-8de2-c171133c279a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55ddb3a2-ce1c-48bc-9287-a9c4b546ad48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostClassifier(random_state=123)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=123)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "clf = AdaBoostClassifier(random_state=123)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3899448f-930d-4c1b-b103-8756c482e98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0\n",
      " 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1\n",
      " 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
      " 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1\n",
      " 0 1 0 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1\n",
      " 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97e41f63-6fa0-4e11-8ef3-680726d5b837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>预测值</th>\n",
       "      <th>实际值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   预测值  实际值\n",
       "0    1    1\n",
       "1    1    1\n",
       "2    1    1\n",
       "3    0    0\n",
       "4    1    1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame() \n",
    "a['预测值'] = list(y_pred)\n",
    "a['实际值'] = list(y_test) # 真实值\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1b5bdae-01be-4a2d-8ca1-1e539b4fd2fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "score = accuracy_score(y_pred, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d9e3ee3-9bf0-4083-bc01-eba971bd8329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.19294615, 0.80705385],\n",
       "       [0.41359387, 0.58640613],\n",
       "       [0.42597039, 0.57402961],\n",
       "       [0.6681739 , 0.3318261 ],\n",
       "       [0.32850159, 0.67149841],\n",
       "       [0.623464  , 0.376536  ],\n",
       "       [0.4311234 , 0.5688766 ],\n",
       "       [0.50649218, 0.49350782],\n",
       "       [0.50405851, 0.49594149],\n",
       "       [0.62764702, 0.37235298]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_proba = clf.predict_proba(X_test)\n",
    "y_pred_proba[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee09265b-bb51-4ddd-b064-f1680dee42a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thres = roc_curve(y_test.values, y_pred_proba[:,1])\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f65e2ce4-9527-4c36-bcca-439332a09cde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9559047909673483\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "score = roc_auc_score(y_test, y_pred_proba[:,1])\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "115f0f01-053c-4f9a-b529-c603dfc60ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18, 0.2 , 0.36, 0.02, 0.24])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f18c824d-6dc5-45fd-addc-e2ec7c70bc0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>特征名称</th>\n",
       "      <th>特征重要性</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>月消费（元）</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>月消费/月收入</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>月收入（元）</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>年龄</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>性别</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      特征名称  特征重要性\n",
       "2   月消费（元）   0.36\n",
       "4  月消费/月收入   0.24\n",
       "1   月收入（元）   0.20\n",
       "0       年龄   0.18\n",
       "3       性别   0.02"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = X.columns  \n",
    "importances = clf.feature_importances_ \n",
    "importances_df = pd.DataFrame()\n",
    "importances_df['特征名称'] = features\n",
    "importances_df['特征重要性'] = importances\n",
    "importances_df.sort_values('特征重要性', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e44b4f7-e1f5-4fb1-b2a0-9a795104a988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.93      0.88       113\n",
      "           1       0.89      0.75      0.81        87\n",
      "\n",
      "    accuracy                           0.85       200\n",
      "   macro avg       0.86      0.84      0.84       200\n",
      "weighted avg       0.85      0.85      0.85       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee18e0ab-b028-487d-ac60-aa154f240db5",
   "metadata": {},
   "source": [
    "#### 我还使用了随机森林进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1816e305-edf9-43cf-ab1b-861ad8f038cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林准确率: 0.84\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.95      0.87       113\n",
      "           1       0.91      0.70      0.79        87\n",
      "\n",
      "    accuracy                           0.84       200\n",
      "   macro avg       0.86      0.82      0.83       200\n",
      "weighted avg       0.85      0.84      0.84       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "rf_clf = RandomForestClassifier(n_estimators=100, random_state=123)\n",
    "rf_clf.fit(X_train, y_train)\n",
    "y_pred_rf = rf_clf.predict(X_test)\n",
    "print(\"随机森林准确率:\", accuracy_score(y_test, y_pred_rf))\n",
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113a44ba-3a83-49ef-9bb7-0c3f89a76212",
   "metadata": {},
   "source": [
    "#### 梯度提升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b40fa256-62c3-474a-a83b-37927c06e5f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "梯度提升准确率: 0.845\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.94      0.87       113\n",
      "           1       0.90      0.72      0.80        87\n",
      "\n",
      "    accuracy                           0.84       200\n",
      "   macro avg       0.86      0.83      0.84       200\n",
      "weighted avg       0.85      0.84      0.84       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gb_clf = GradientBoostingClassifier(n_estimators=100, random_state=123)\n",
    "gb_clf.fit(X_train, y_train)\n",
    "y_pred_gb = gb_clf.predict(X_test)\n",
    "print(\"梯度提升准确率:\", accuracy_score(y_test, y_pred_gb))\n",
    "print(classification_report(y_test, y_pred_gb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a5b04f-1635-47c6-9343-efb75ab01f4c",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "81735423-a7f7-4daf-b324-732ef86af189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM准确率: 0.845\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.95      0.87       113\n",
      "           1       0.91      0.71      0.80        87\n",
      "\n",
      "    accuracy                           0.84       200\n",
      "   macro avg       0.86      0.83      0.84       200\n",
      "weighted avg       0.85      0.84      0.84       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "svm_clf = SVC(kernel='rbf', random_state=123)\n",
    "svm_clf.fit(X_train_scaled, y_train)\n",
    "y_pred_svm = svm_clf.predict(X_test_scaled)\n",
    "print(\"SVM准确率:\", accuracy_score(y_test, y_pred_svm))\n",
    "print(classification_report(y_test, y_pred_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d67a2e-1adb-4048-912b-db99904e2852",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e67c9f13-0aed-4a83-83e6-5d3a639ee92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost准确率: 0.81\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.87      0.84       113\n",
      "           1       0.81      0.74      0.77        87\n",
      "\n",
      "    accuracy                           0.81       200\n",
      "   macro avg       0.81      0.80      0.80       200\n",
      "weighted avg       0.81      0.81      0.81       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_clf = XGBClassifier(n_estimators=100, random_state=123)\n",
    "xgb_clf.fit(X_train, y_train)\n",
    "y_pred_xgb = xgb_clf.predict(X_test)\n",
    "print(\"XGBoost准确率:\", accuracy_score(y_test, y_pred_xgb))\n",
    "print(classification_report(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbae87c7-5a52-4f09-9c2e-df78584910b9",
   "metadata": {},
   "source": [
    "#### 逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f111bfc8-5688-4097-b682-6c4589f45f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归准确率: 0.83\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.87      0.85       113\n",
      "           1       0.82      0.78      0.80        87\n",
      "\n",
      "    accuracy                           0.83       200\n",
      "   macro avg       0.83      0.82      0.83       200\n",
      "weighted avg       0.83      0.83      0.83       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_clf = LogisticRegression(random_state=123)\n",
    "lr_clf.fit(X_train, y_train)\n",
    "y_pred_lr = lr_clf.predict(X_test)\n",
    "print(\"逻辑回归准确率:\", accuracy_score(y_test, y_pred_lr))\n",
    "print(classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438b00bb-cc12-457b-89c9-64725b40dabb",
   "metadata": {},
   "source": [
    "#### 将所有的进行比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04d7d213-f193-43ae-b49f-ed93d93aff60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型性能比较:\n",
      "AdaBoost: 0.8500\n",
      "Random Forest: 0.8400\n",
      "Gradient Boosting: 0.8450\n",
      "XGBoost: 0.8100\n",
      "Logistic Regression: 0.8300\n"
     ]
    }
   ],
   "source": [
    "# 比较所有模型\n",
    "models = {\n",
    "    'AdaBoost':clf,\n",
    "    'Random Forest': rf_clf,\n",
    "    'Gradient Boosting': gb_clf,\n",
    "    'XGBoost': xgb_clf,\n",
    "    'Logistic Regression': lr_clf\n",
    "}\n",
    "results = {}\n",
    "\n",
    "print(\"模型性能比较:\")\n",
    "for name, model in models.items():\n",
    "    if name == 'SVM':\n",
    "        y_pred = model.predict(X_test_scaled)\n",
    "    else:\n",
    "        y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(f\"{name}: {accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95885d75-84fe-4eb7-9065-6d1d9ad3ab36",
   "metadata": {},
   "source": [
    "#####\n",
    "AdaBoost表现最佳：在信用卡精准营销这个任务上，AdaBoost的预测准确率最高 \n",
    "\r\n",
    "集成学习方法整体表现优异：前三名都是集成学习 \n",
    "\r\n",
    "\r\n",
    "传统逻辑回归仍有竞争力：虽然不如集成学习方法，但0.83的准确率 \n",
    "不错\r\n",
    "\r\n",
    "XGBoost表现相对较差：这可能是因为参数需要调优，或者数据特征不适合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "62bdb81e-1d58-4ad5-a66b-bb51e7fd249a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, \n",
    "                           f1_score, roc_auc_score, confusion_matrix, \n",
    "                           classification_report, roc_curve, precision_recall_curve)\n",
    "from sklearn.preprocessing import label_binarize\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "sns.set_style(\"whitegrid\")\n",
    "for name, model in models.items():\n",
    "    if name == 'SVM':\n",
    "        y_pred = model.predict(X_test_scaled)\n",
    "        y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]\n",
    "    else:\n",
    "        y_pred = model.predict(X_test)\n",
    "        y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred_proba)\n",
    "    results[name] = {\n",
    "        'accuracy': accuracy,\n",
    "        'precision': precision,\n",
    "        'recall': recall,\n",
    "        'f1': f1,\n",
    "        'auc': auc,\n",
    "        'y_pred': y_pred,\n",
    "        'y_pred_proba': y_pred_proba\n",
    "    }\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# 1.1 主要指标对比\n",
    "metrics = ['accuracy', 'precision', 'recall', 'f1']\n",
    "plt.subplot(2, 3, 1)\n",
    "x = np.arange(len(models))\n",
    "width = 0.2\n",
    "for i, metric in enumerate(metrics):\n",
    "    values = [results[name][metric] for name in models]\n",
    "    plt.bar(x + i*width, values, width, label=metric, alpha=0.8)\n",
    "plt.xlabel('模型')\n",
    "plt.ylabel('分数')\n",
    "plt.title('模型性能指标对比')\n",
    "plt.xticks(x + width*1.5, models.keys(), rotation=45)\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "# 1.2 AUC对比\n",
    "plt.subplot(2, 3, 2)\n",
    "auc_values = [results[name]['auc'] for name in models]\n",
    "plt.bar(models.keys(), auc_values, color='lightcoral', alpha=0.8)\n",
    "plt.xlabel('模型')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('模型AUC对比')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, alpha=0.3)\n",
    "# 2. ROC曲线\n",
    "plt.subplot(2, 3, 3)\n",
    "for name in models:\n",
    "    fpr, tpr, _ = roc_curve(y_test, results[name]['y_pred_proba'])\n",
    "    plt.plot(fpr, tpr, label=f'{name} (AUC = {results[name][\"auc\"]:.3f})', linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "plt.xlabel('假正率 (False Positive Rate)')\n",
    "plt.ylabel('真正率 (True Positive Rate)')\n",
    "plt.title('ROC曲线比较')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "# 3. 精确率-召回率曲线\n",
    "plt.subplot(2, 3, 4)\n",
    "for name in models:\n",
    "    precision_curve, recall_curve, _ = precision_recall_curve(y_test, results[name]['y_pred_proba'])\n",
    "    plt.plot(recall_curve, precision_curve, label=name, linewidth=2)\n",
    "plt.xlabel('召回率 (Recall)')\n",
    "plt.ylabel('精确率 (Precision)')\n",
    "plt.title('精确率-召回率曲线')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "# 4. 模型排名热力图\n",
    "plt.subplot(2, 3, 5)\n",
    "metrics_rank = ['accuracy', 'precision', 'recall', 'f1', 'auc']\n",
    "rank_data = []\n",
    "for metric in metrics_rank:\n",
    "    metric_values = [results[name][metric] for name in models]\n",
    "    # 计算排名（从高到低）\n",
    "    ranks = len(models) - np.argsort(np.argsort(metric_values))\n",
    "    rank_data.append(ranks)\n",
    "rank_data = np.array(rank_data)\n",
    "sns.heatmap(rank_data, annot=True, fmt='d', cmap='YlOrRd', \n",
    "            xticklabels=models.keys(), yticklabels=metrics_rank,\n",
    "            cbar_kws={'label': '排名 (1=最好)'})\n",
    "plt.title('模型各项指标排名\\n(数字越小表现越好)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.subplot(2, 3, 6, polar=True)\n",
    "# 归一化指标\n",
    "metrics_for_radar = ['accuracy', 'precision', 'recall', 'f1', 'auc']\n",
    "angles = np.linspace(0, 2*np.pi, len(metrics_for_radar), endpoint=False).tolist()\n",
    "angles += angles[:1]  # 闭合图形\n",
    "for i, name in enumerate(models):\n",
    "    values = [results[name][metric] for metric in metrics_for_radar]\n",
    "    values += values[:1]  # 闭合图形\n",
    "    plt.plot(angles, values, 'o-', linewidth=2, label=name, markersize=4)\n",
    "plt.fill(angles, [1]*len(angles), alpha=0.1)  # 参考区域\n",
    "plt.thetagrids(np.degrees(angles[:-1]), metrics_for_radar)\n",
    "plt.title('模型综合性能雷达图')\n",
    "plt.legend(bbox_to_anchor=(1.2, 1.0))\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aee4b7e-5f01-4af4-9941-bf37c41cad4e",
   "metadata": {},
   "source": [
    "### 案例分析：产品定价模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c74fdcf-fd27-412d-933d-fb1149cc422f",
   "metadata": {},
   "source": [
    "#### 读取数据，建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "907968f4-a406-4f37-b19f-709c94d5b41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>页数</th>\n",
       "      <th>类别</th>\n",
       "      <th>彩印</th>\n",
       "      <th>纸张</th>\n",
       "      <th>价格</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>207</td>\n",
       "      <td>技术类</td>\n",
       "      <td>0</td>\n",
       "      <td>双胶纸</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210</td>\n",
       "      <td>技术类</td>\n",
       "      <td>0</td>\n",
       "      <td>双胶纸</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206</td>\n",
       "      <td>技术类</td>\n",
       "      <td>0</td>\n",
       "      <td>双胶纸</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>218</td>\n",
       "      <td>技术类</td>\n",
       "      <td>0</td>\n",
       "      <td>双胶纸</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>209</td>\n",
       "      <td>技术类</td>\n",
       "      <td>0</td>\n",
       "      <td>双胶纸</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    页数   类别  彩印   纸张  价格\n",
       "0  207  技术类   0  双胶纸  60\n",
       "1  210  技术类   0  双胶纸  62\n",
       "2  206  技术类   0  双胶纸  62\n",
       "3  218  技术类   0  双胶纸  64\n",
       "4  209  技术类   0  双胶纸  60"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('产品定价模型.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "68c32cb4-2bab-4f76-a079-4f00187f748a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "类别\n",
       "技术类    336\n",
       "教辅类    333\n",
       "办公类    331\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['类别'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8fb2afba-a742-4fa6-96d2-bef361672097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "彩印\n",
       "0    648\n",
       "1    352\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['彩印'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "297bc1e2-3470-4570-bb75-709172d99365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "纸张\n",
       "双胶纸    615\n",
       "铜版纸    196\n",
       "书写纸    189\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['纸张'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "514b98a4-263b-4158-9167-182eaf6ec2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['类别'] = le.fit_transform(df['类别'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9dfae53b-52af-4901-b3bb-88ca8352b11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "类别\n",
       "1    336\n",
       "2    333\n",
       "0    331\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['类别'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9219bc9b-38f7-41bd-82f8-d16b13e28135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "纸张\n",
       "1    615\n",
       "2    196\n",
       "0    189\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df['纸张'] = le.fit_transform(df['纸张'])\n",
    "df['纸张'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a373e91d-525c-4665-b7b9-dcd629f7b8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>页数</th>\n",
       "      <th>类别</th>\n",
       "      <th>彩印</th>\n",
       "      <th>纸张</th>\n",
       "      <th>价格</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>207</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>209</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    页数  类别  彩印  纸张  价格\n",
       "0  207   1   0   1  60\n",
       "1  210   1   0   1  62\n",
       "2  206   1   0   1  62\n",
       "3  218   1   0   1  64\n",
       "4  209   1   0   1  60"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6e19f6e2-144f-4579-bc3e-0a5a67849a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 71.15004038  79.56199921  68.21751792  90.78788507  78.88479128\n",
      "  42.28022702  39.27334177  60.74670841  53.59744659  77.65931771\n",
      "  80.22295545  76.04437155  79.56199921  58.40372895  79.65245266\n",
      "  44.27997693  53.18177447  35.31452467  92.1798291   58.40372895\n",
      "  41.96644278  99.50466356  80.22295545  79.69648341  91.45061741\n",
      "  42.93885741  42.86973046  75.71824996  48.55203652  62.94185778\n",
      "  39.47077874  61.54190648  95.18389309  51.88118394  65.1293139\n",
      "  50.17577837  39.54495179  83.63542315  56.24632221 102.1176112\n",
      "  48.89080247  49.23639342  33.03502962  52.74862135  35.47220867\n",
      "  35.00370671  53.9446399   74.62364353  35.31452467  53.9446399 ]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns='价格')   # 特征变量\n",
    "y = df['价格']     # 目标变量\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "model = GradientBoostingRegressor(random_state=123)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(y_pred[0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "18841a09-71ee-4bc7-b683-1e521d7a8d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>预测值</th>\n",
       "      <th>实际值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.150040</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.561999</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.217518</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.787885</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.884791</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         预测值  实际值\n",
       "0  71.150040   75\n",
       "1  79.561999   84\n",
       "2  68.217518   68\n",
       "3  90.787885   90\n",
       "4  78.884791   85"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame()  # 创建一个空DataFrame \n",
    "a['预测值'] = list(y_pred)\n",
    "a['实际值'] = list(y_test)\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6067029c-596c-45b7-99bb-a2d2d2e1218a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49070203, 0.44718694, 0.04161545, 0.02049558])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d598a04-d9f1-42b6-8259-2e170f16740c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>特征名称</th>\n",
       "      <th>特征重要性</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>页数</td>\n",
       "      <td>0.490702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>类别</td>\n",
       "      <td>0.447187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>彩印</td>\n",
       "      <td>0.041615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>纸张</td>\n",
       "      <td>0.020496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  特征名称     特征重要性\n",
       "0   页数  0.490702\n",
       "1   类别  0.447187\n",
       "2   彩印  0.041615\n",
       "3   纸张  0.020496"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = X.columns  # 获取特征名称\n",
    "importances = model.feature_importances_  # 获取特征重要性\n",
    "importances_df = pd.DataFrame()\n",
    "importances_df['特征名称'] = features\n",
    "importances_df['特征重要性'] = importances\n",
    "importances_df.sort_values('特征重要性', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9890e034-f9af-4daf-a766-ee314d42a6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据预处理...\n",
      "类别 编码映射: {0: 0, 1: 1, 2: 2}\n",
      "纸张 编码映射: {0: 0, 1: 1, 2: 2}\n",
      "训练集大小: (800, 4), 测试集大小: (200, 4)\n",
      "价格范围: 20 - 114\n",
      "\n",
      "训练模型中...\n",
      "  GBDT: R² = 0.8465, RMSE = 8.2520\n",
      "  随机森林: R² = 0.8503, RMSE = 8.1483\n",
      "  XGBoost: R² = 0.8366, RMSE = 8.5139\n",
      "  线性回归: R² = 0.5188, RMSE = 14.6111\n",
      "  岭回归: R² = 0.5189, RMSE = 14.6097\n",
      "  Lasso回归: R² = 0.5153, RMSE = 14.6645\n",
      "  决策树: R² = 0.8062, RMSE = 9.2720\n",
      "  AdaBoost: R² = 0.7686, RMSE = 10.1322\n",
      "  SVR: R² = 0.7844, RMSE = 9.7810\n",
      "\n",
      "================================================================================\n",
      "产品定价模型性能总结\n",
      "================================================================================\n",
      "模型           R²       RMSE     MAE      MSE      排名    \n",
      "--------------------------------------------------------------------------------\n",
      "随机森林         0.8503   8.1483   4.3206   66.3955  1     \n",
      "GBDT         0.8465   8.2520   4.8346   68.0959  2     \n",
      "XGBoost      0.8366   8.5139   4.6818   72.4859  3     \n",
      "决策树          0.8062   9.2720   4.7758   85.9704  4     \n",
      "SVR          0.7844   9.7810   6.9071   95.6681  5     \n",
      "AdaBoost     0.7686   10.1322  8.0073   102.6606 6     \n",
      "岭回归          0.5189   14.6097  12.4003  213.4436 7     \n",
      "线性回归         0.5188   14.6111  12.4014  213.4850 8     \n",
      "Lasso回归      0.5153   14.6645  12.2343  215.0461 9     \n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import xgboost as xgb\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "sns.set_style(\"whitegrid\")\n",
    "print(\"数据预处理...\")\n",
    "label_encoders = {}\n",
    "categorical_columns = ['类别', '纸张']\n",
    "for col in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    df[col] = le.fit_transform(df[col])\n",
    "    label_encoders[col] = le\n",
    "    print(f\"{col} 编码映射: {dict(zip(le.classes_, le.transform(le.classes_)))}\")\n",
    "X = df.drop('价格', axis=1)\n",
    "y = df['价格']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "print(f\"训练集大小: {X_train.shape}, 测试集大小: {X_test.shape}\")\n",
    "print(f\"价格范围: {y.min()} - {y.max()}\")\n",
    "models = {\n",
    "    'GBDT': GradientBoostingRegressor(random_state=42),\n",
    "    '随机森林': RandomForestRegressor(random_state=42),\n",
    "    'XGBoost': xgb.XGBRegressor(random_state=42),\n",
    "    '线性回归': LinearRegression(),\n",
    "    '岭回归': Ridge(random_state=42),\n",
    "    'Lasso回归': Lasso(random_state=42),\n",
    "    '决策树': DecisionTreeRegressor(random_state=42),\n",
    "    'AdaBoost': AdaBoostRegressor(random_state=42),\n",
    "    'SVR': SVR()\n",
    "}\n",
    "results = {}\n",
    "predictions = {}\n",
    "print(\"\\n训练模型中...\")\n",
    "for name, model in models.items():\n",
    "    try:\n",
    "        if name == 'SVR':\n",
    "            model.fit(X_train_scaled, y_train)\n",
    "            y_pred = model.predict(X_test_scaled)\n",
    "        else:\n",
    "            model.fit(X_train, y_train)\n",
    "            y_pred = model.predict(X_test)\n",
    "        mse = mean_squared_error(y_test, y_pred)\n",
    "        mae = mean_absolute_error(y_test, y_pred)\n",
    "        r2 = r2_score(y_test, y_pred)\n",
    "        rmse = np.sqrt(mse)\n",
    "        results[name] = {\n",
    "            'MSE': mse,\n",
    "            'RMSE': rmse,\n",
    "            'MAE': mae,\n",
    "            'R2': r2\n",
    "        }\n",
    "        predictions[name] = y_pred\n",
    "        print(f\"  {name}: R² = {r2:.4f}, RMSE = {rmse:.4f}\")\n",
    "    except Exception as e:\n",
    "        print(f\"  {name} 训练失败: {e}\")\n",
    "# ==================== 可视化结果 ====================\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"产品定价模型性能总结\")\n",
    "print(\"=\"*80)\n",
    "print(f\"{'模型':<12} {'R²':<8} {'RMSE':<8} {'MAE':<8} {'MSE':<8} {'排名':<6}\")\n",
    "print(\"-\"*80)\n",
    "sorted_results = sorted(results.items(), key=lambda x: x[1]['R2'], reverse=True)\n",
    "for rank, (name, metrics) in enumerate(sorted_results, 1):\n",
    "    print(f\"{name:<12} {metrics['R2']:<8.4f} {metrics['RMSE']:<8.4f} \"\n",
    "          f\"{metrics['MAE']:<8.4f} {metrics['MSE']:<8.4f} {rank:<6}\")\n",
    "\n",
    "print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebfdab6-5284-430b-9550-b8b88883235c",
   "metadata": {},
   "source": [
    "#### 模型选择推荐\n",
    "    '首选': '随机森林',\r\n",
    "    '备选': 'GBDT', \r\n",
    "    '理由': 'R² > 0.84，预测误差小，稳定性好'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55a0467-afd1-4cd8-9d1c-646823c2a701",
   "metadata": {},
   "source": [
    "#### 定价预测精度评估\r",
    "    \n",
    "平均误差：4-5元（对于图书定价来说精度不错\r",
    "    \n",
    "\r\n",
    "最大可能误差：约8-10元（RME    ）\r\n",
    "\r\n",
    "业务适用性：✅ 完全可以用于实际定价指导"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34ca1944-a2d3-475c-aa62-85654eba4fab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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